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A particle filter using SVD based sampling Kalman filter to obtain the proposal distribution

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3 Author(s)
Bin Liu ; Graduate University, The Chinese Academy of Sciences, Institute of Acoustics, The Chinese Academy of Sciences, Beijing, China ; Xiao-chuan Ma ; Chao-huan Hou

In this paper, we propose a novel particle filter (PF), which uses a bank of singular-value-decomposition based sampling Kalman filters (SVDSKF) to obtain the importance proposal distribution. This proposal has two properties. Firstly, it allows the particle filter to incorporate the latest observations into a prior updating routine and, secondly it inherits advantage of having good numerical stability from the singular-value-decomposition (SVD). The convergence results of the numerical simulations we made confirm that the proposed PF method outperforms the standard bootstrap PF as well as other local linearization based PFs.

Published in:

2008 IEEE Conference on Cybernetics and Intelligent Systems

Date of Conference:

21-24 Sept. 2008